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58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
# Titan Robotics Team 2022: CorrelationTest submodule
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# Written by Arthur Lu
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# Notes:
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# this should be imported as a python module using 'from tra_analysis import CorrelationTest'
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# setup:
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__version__ = "1.0.0"
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__changelog__ = """changelog:
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1.0.0:
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- ported analysis.CorrelationTest() here
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- removed classness
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"""
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__author__ = (
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"Arthur Lu <learthurgo@gmail.com>",
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)
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__all__ = [
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]
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import scipy
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from scipy import stats
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def anova_oneway(self, *args): #expects arrays of samples
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results = scipy.stats.f_oneway(*args)
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return {"f-value": results[0], "p-value": results[1]}
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def pearson(self, x, y):
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results = scipy.stats.pearsonr(x, y)
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return {"r-value": results[0], "p-value": results[1]}
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def spearman(self, a, b = None, axis = 0, nan_policy = 'propagate'):
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results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy)
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return {"r-value": results[0], "p-value": results[1]}
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def point_biserial(self, x,y):
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results = scipy.stats.pointbiserialr(x, y)
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return {"r-value": results[0], "p-value": results[1]}
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def kendall(self, x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'):
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results = scipy.stats.kendalltau(x, y, initial_lexsort = initial_lexsort, nan_policy = nan_policy, method = method)
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return {"tau": results[0], "p-value": results[1]}
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def kendall_weighted(self, x, y, rank = True, weigher = None, additive = True):
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results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive)
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return {"tau": results[0], "p-value": results[1]}
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def mgc(self, x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None):
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results = scipy.stats.multiscale_graphcorr(x, y, compute_distance = compute_distance, reps = reps, workers = workers, is_twosamp = is_twosamp, random_state = random_state)
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return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value |